Comparing Dataset A and Dataset B

Dataset statistics

	Dataset A	Dataset B
Number of variables	12	12
Number of observations	446	446
Missing cells	443	426
Missing cells (%)	8.3%	8.0%
Duplicate rows	0	0
Duplicate rows (%)	0.0%	0.0%
Total size in memory	45.3 KiB	45.3 KiB
Average record size in memory	104.0 B	104.0 B

Variable types

	Dataset A	Dataset B
Numeric	5	5
Categorical	4	4
Text	3	3

Alerts

Dataset A	Dataset B
`Age` has 89 (20.0%) missing values	`Age` has 83 (18.6%) missing values	Missing
`Cabin` has 353 (79.1%) missing values	`Cabin` has 342 (76.7%) missing values	Missing
`PassengerId` has unique values	`PassengerId` has unique values	Unique
`Name` has unique values	`Name` has unique values	Unique
`SibSp` has 299 (67.0%) zeros	`SibSp` has 296 (66.4%) zeros	Zeros
`Parch` has 340 (76.2%) zeros	`Parch` has 329 (73.8%) zeros	Zeros
`Fare` has 7 (1.6%) zeros	`Fare` has 6 (1.3%) zeros	Zeros

Reproduction

	Dataset A	Dataset B
Analysis started	2023-12-07 16:08:43.182664	2023-12-07 16:08:46.856741
Analysis finished	2023-12-07 16:08:46.855711	2023-12-07 16:08:50.740655
Duration	3.67 seconds	3.88 seconds
Software version	ydata-profiling v0.0.dev0	ydata-profiling v0.0.dev0
Download configuration	config.json	config.json

PassengerId
Real number (ℝ)

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	443.47534	437.16592

	Dataset A	Dataset B
Minimum	3	1
Maximum	889	890
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	3	1
5-th percentile	37.25	49.25
Q1	235.25	221.5
median	439	430.5
Q3	658.5	654.75
95-th percentile	841.5	841.75
Maximum	889	890
Range	886	889
Interquartile range (IQR)	423.25	433.25

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	253.59671	251.97954
Coefficient of variation (CV)	0.5718395	0.57639338
Kurtosis	-1.1294502	-1.1453682
Mean	443.47534	437.16592
Median Absolute Deviation (MAD)	211	216
Skewness	-0.02666757	0.034909975
Sum	197790	194976
Variance	64311.293	63493.689
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
604	1	0.2%
666	1	0.2%
220	1	0.2%
573	1	0.2%
308	1	0.2%
707	1	0.2%
166	1	0.2%
800	1	0.2%
727	1	0.2%
463	1	0.2%
Other values (436)	436	97.8%

Value	Count	Frequency (%)
415	1	0.2%
449	1	0.2%
92	1	0.2%
165	1	0.2%
107	1	0.2%
670	1	0.2%
592	1	0.2%
772	1	0.2%
409	1	0.2%
853	1	0.2%
Other values (436)	436	97.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
3	1	0.2%
4	1	0.2%
5	1	0.2%
6	1	0.2%
8	1	0.2%
9	1	0.2%
12	1	0.2%
14	1	0.2%
15	1	0.2%
16	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
4	1	0.2%
6	1	0.2%
9	1	0.2%
10	1	0.2%
11	1	0.2%
12	1	0.2%
19	1	0.2%
23	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
4	1	0.2%
6	1	0.2%
9	1	0.2%
10	1	0.2%
11	1	0.2%
12	1	0.2%
19	1	0.2%
23	1	0.2%

Value	Count	Frequency (%)
3	1	0.2%
4	1	0.2%
5	1	0.2%
6	1	0.2%
8	1	0.2%
9	1	0.2%
12	1	0.2%
14	1	0.2%
15	1	0.2%
16	1	0.2%

Survived
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

0	280
1	166

0	273
1	173

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	2	2
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	0	1
2nd row	0	1
3rd row	0	1
4th row	0	1
5th row	1	1

Common Values

Value	Count	Frequency (%)
0	280	62.8%
1	166	37.2%

Value	Count	Frequency (%)
0	273	61.2%
1	173	38.8%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
0	280	62.8%
1	166	37.2%

Value	Count	Frequency (%)
0	273	61.2%
1	173	38.8%

Most occurring characters

Value	Count	Frequency (%)
0	280	62.8%
1	166	37.2%

Value	Count	Frequency (%)
0	273	61.2%
1	173	38.8%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	280	62.8%
1	166	37.2%

Value	Count	Frequency (%)
0	273	61.2%
1	173	38.8%

Most occurring scripts

Value	Count	Frequency (%)
Common	446	100.0%

Value	Count	Frequency (%)
Common	446	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	280	62.8%
1	166	37.2%

Value	Count	Frequency (%)
0	273	61.2%
1	173	38.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	446	100.0%

Value	Count	Frequency (%)
ASCII	446	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	280	62.8%
1	166	37.2%

Value	Count	Frequency (%)
0	273	61.2%
1	173	38.8%

Pclass
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

3	240
1	103
2	103

3	240
1	112
2	94

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	3	3
2nd row	3	2
3rd row	3	1
4th row	1	1
5th row	3	2

Common Values

Value	Count	Frequency (%)
3	240	53.8%
1	103	23.1%
2	103	23.1%

Value	Count	Frequency (%)
3	240	53.8%
1	112	25.1%
2	94	21.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
3	240	53.8%
1	103	23.1%
2	103	23.1%

Value	Count	Frequency (%)
3	240	53.8%
1	112	25.1%
2	94	21.1%

Most occurring characters

Value	Count	Frequency (%)
3	240	53.8%
1	103	23.1%
2	103	23.1%

Value	Count	Frequency (%)
3	240	53.8%
1	112	25.1%
2	94	21.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
3	240	53.8%
1	103	23.1%
2	103	23.1%

Value	Count	Frequency (%)
3	240	53.8%
1	112	25.1%
2	94	21.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	446	100.0%

Value	Count	Frequency (%)
Common	446	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
3	240	53.8%
1	103	23.1%
2	103	23.1%

Value	Count	Frequency (%)
3	240	53.8%
1	112	25.1%
2	94	21.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	446	100.0%

Value	Count	Frequency (%)
ASCII	446	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	240	53.8%
1	103	23.1%
2	103	23.1%

Value	Count	Frequency (%)
3	240	53.8%
1	112	25.1%
2	94	21.1%

Name
['Text', 'Text']

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	82	82
Median length	51	49
Mean length	27.204036	27.802691
Min length	12	12

Characters and Unicode

	Dataset A	Dataset B
Total characters	12133	12400
Distinct characters	60	59
Distinct categories	7	7 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	446	446 ?
Unique (%)	100.0%	100.0%

Sample

	Dataset A	Dataset B
1st row	Torber, Mr. Ernst William	Sundman, Mr. Johan Julian
2nd row	Kink, Mr. Vincenz	Ridsdale, Miss. Lucy
3rd row	Kilgannon, Mr. Thomas J	Goldenberg, Mr. Samuel L
4th row	Graham, Mr. George Edward	Daly, Mr. Peter Denis
5th row	Devaney, Miss. Margaret Delia	Trout, Mrs. William H (Jessie L)

Value	Count	Frequency (%)
mr	261	14.3%
miss	90	4.9%
mrs	63	3.5%
william	35	1.9%
master	22	1.2%
john	21	1.2%
henry	20	1.1%
charles	15	0.8%
george	13	0.7%
james	12	0.7%
Other values (897)	1271	69.7%

Value	Count	Frequency (%)
mr	253	13.6%
miss	87	4.7%
mrs	77	4.1%
john	28	1.5%
william	28	1.5%
master	22	1.2%
george	15	0.8%
henry	14	0.8%
charles	13	0.7%
james	12	0.6%
Other values (918)	1310	70.5%

Most occurring characters

Value	Count	Frequency (%)
	1378	11.4%
r	1007	8.3%
e	898	7.4%
a	821	6.8%
i	680	5.6%
n	659	5.4%
s	650	5.4%
M	559	4.6%
l	514	4.2%
o	490	4.0%
Other values (50)	4477	36.9%

Value	Count	Frequency (%)
	1414	11.4%
r	1021	8.2%
a	880	7.1%
e	874	7.0%
i	698	5.6%
n	658	5.3%
s	654	5.3%
M	566	4.6%
l	538	4.3%
o	516	4.2%
Other values (49)	4581	36.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7812	64.4%
Uppercase Letter	1834	15.1%
Space Separator	1378	11.4%
Other Punctuation	966	8.0%
Open Punctuation	68	0.6%
Close Punctuation	68	0.6%
Dash Punctuation	7	0.1%

Value	Count	Frequency (%)
Lowercase Letter	7989	64.4%
Uppercase Letter	1865	15.0%
Space Separator	1414	11.4%
Other Punctuation	953	7.7%
Close Punctuation	86	0.7%
Open Punctuation	86	0.7%
Dash Punctuation	7	0.1%

Most frequent character per category

Space Separator

Value	Count	Frequency (%)
	1378	100.0%

Value	Count	Frequency (%)
	1414	100.0%

Lowercase Letter

Value	Count	Frequency (%)
r	1007	12.9%
e	898	11.5%
a	821	10.5%
i	680	8.7%
n	659	8.4%
s	650	8.3%
l	514	6.6%
o	490	6.3%
t	340	4.4%
h	265	3.4%
Other values (16)	1488	19.0%

Value	Count	Frequency (%)
r	1021	12.8%
a	880	11.0%
e	874	10.9%
i	698	8.7%
n	658	8.2%
s	654	8.2%
l	538	6.7%
o	516	6.5%
t	350	4.4%
h	279	3.5%
Other values (16)	1521	19.0%

Uppercase Letter

Value	Count	Frequency (%)
M	559	30.5%
A	124	6.8%
J	107	5.8%
H	103	5.6%
S	102	5.6%
C	90	4.9%
W	80	4.4%
E	79	4.3%
L	63	3.4%
B	61	3.3%
Other values (15)	466	25.4%

Value	Count	Frequency (%)
M	566	30.3%
A	127	6.8%
J	112	6.0%
H	100	5.4%
S	92	4.9%
C	92	4.9%
E	83	4.5%
B	74	4.0%
L	72	3.9%
R	67	3.6%
Other values (15)	480	25.7%

Other Punctuation

Value	Count	Frequency (%)
.	447	46.3%
,	446	46.2%
"	66	6.8%
'	6	0.6%
/	1	0.1%

Value	Count	Frequency (%)
.	447	46.9%
,	446	46.8%
"	56	5.9%
'	4	0.4%

Open Punctuation

Value	Count	Frequency (%)
(	68	100.0%

Value	Count	Frequency (%)
(	86	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	68	100.0%

Value	Count	Frequency (%)
)	86	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	7	100.0%

Value	Count	Frequency (%)
-	7	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	9646	79.5%
Common	2487	20.5%

Value	Count	Frequency (%)
Latin	9854	79.5%
Common	2546	20.5%

Most frequent character per script

Common

Value	Count	Frequency (%)
	1378	55.4%
.	447	18.0%
,	446	17.9%
(	68	2.7%
)	68	2.7%
"	66	2.7%
-	7	0.3%
'	6	0.2%
/	1	< 0.1%

Value	Count	Frequency (%)
	1414	55.5%
.	447	17.6%
,	446	17.5%
)	86	3.4%
(	86	3.4%
"	56	2.2%
-	7	0.3%
'	4	0.2%

Latin

Value	Count	Frequency (%)
r	1007	10.4%
e	898	9.3%
a	821	8.5%
i	680	7.0%
n	659	6.8%
s	650	6.7%
M	559	5.8%
l	514	5.3%
o	490	5.1%
t	340	3.5%
Other values (41)	3028	31.4%

Value	Count	Frequency (%)
r	1021	10.4%
a	880	8.9%
e	874	8.9%
i	698	7.1%
n	658	6.7%
s	654	6.6%
M	566	5.7%
l	538	5.5%
o	516	5.2%
t	350	3.6%
Other values (41)	3099	31.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	12133	100.0%

Value	Count	Frequency (%)
ASCII	12400	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1378	11.4%
r	1007	8.3%
e	898	7.4%
a	821	6.8%
i	680	5.6%
n	659	5.4%
s	650	5.4%
M	559	4.6%
l	514	4.2%
o	490	4.0%
Other values (50)	4477	36.9%

Value	Count	Frequency (%)
	1414	11.4%
r	1021	8.2%
a	880	7.1%
e	874	7.0%
i	698	5.6%
n	658	5.3%
s	654	5.3%
M	566	4.6%
l	538	4.3%
o	516	4.2%
Other values (49)	4581	36.9%

Sex
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

male	291
female	155

male	280
female	166

Length

	Dataset A	Dataset B
Max length	6	6
Median length	4	4
Mean length	4.6950673	4.7443946
Min length	4	4

Characters and Unicode

	Dataset A	Dataset B
Total characters	2094	2116
Distinct characters	5	5
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	male	male
2nd row	male	female
3rd row	male	male
4th row	male	male
5th row	female	female

Common Values

Value	Count	Frequency (%)
male	291	65.2%
female	155	34.8%

Value	Count	Frequency (%)
male	280	62.8%
female	166	37.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
male	291	65.2%
female	155	34.8%

Value	Count	Frequency (%)
male	280	62.8%
female	166	37.2%

Most occurring characters

Value	Count	Frequency (%)
e	601	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	155	7.4%

Value	Count	Frequency (%)
e	612	28.9%
m	446	21.1%
a	446	21.1%
l	446	21.1%
f	166	7.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	2094	100.0%

Value	Count	Frequency (%)
Lowercase Letter	2116	100.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	601	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	155	7.4%

Value	Count	Frequency (%)
e	612	28.9%
m	446	21.1%
a	446	21.1%
l	446	21.1%
f	166	7.8%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2094	100.0%

Value	Count	Frequency (%)
Latin	2116	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	601	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	155	7.4%

Value	Count	Frequency (%)
e	612	28.9%
m	446	21.1%
a	446	21.1%
l	446	21.1%
f	166	7.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2094	100.0%

Value	Count	Frequency (%)
ASCII	2116	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	601	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	155	7.4%

Value	Count	Frequency (%)
e	612	28.9%
m	446	21.1%
a	446	21.1%
l	446	21.1%
f	166	7.8%

Age
Real number (ℝ)

	Dataset A	Dataset B
Distinct	77	72
Distinct (%)	21.6%	19.8%
Missing	89	83
Missing (%)	20.0%	18.6%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	30.441653	29.488044

	Dataset A	Dataset B
Minimum	0.42	0.75
Maximum	80	74
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0.42	0.75
5-th percentile	5.8	4.1
Q1	21	20
median	29	28
Q3	39	38
95-th percentile	56.2	55.35
Maximum	80	74
Range	79.58	73.25
Interquartile range (IQR)	18	18

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	14.568429	14.450168
Coefficient of variation (CV)	0.47856892	0.4900348
Kurtosis	0.37075641	0.048340959
Mean	30.441653	29.488044
Median Absolute Deviation (MAD)	9	9
Skewness	0.45680885	0.36328728
Sum	10867.67	10704.16
Variance	212.23912	208.80735
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
28	16	3.6%
22	14	3.1%
25	14	3.1%
19	14	3.1%
24	14	3.1%
18	12	2.7%
30	12	2.7%
31	11	2.5%
36	11	2.5%
21	11	2.5%
Other values (67)	228	51.1%
(Missing)	89	20.0%

Value	Count	Frequency (%)
24	15	3.4%
28	15	3.4%
22	15	3.4%
19	14	3.1%
30	14	3.1%
25	14	3.1%
36	12	2.7%
18	12	2.7%
21	12	2.7%
26	11	2.5%
Other values (62)	229	51.3%
(Missing)	83	18.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0.42	1	0.2%
0.83	1	0.2%
0.92	1	0.2%
1	1	0.2%
2	4	0.9%
3	4	0.9%
4	5	1.1%
5	1	0.2%
6	1	0.2%
7	1	0.2%

Value	Count	Frequency (%)
0.75	2	0.4%
0.83	2	0.4%
1	3	0.7%
2	6	1.3%
3	1	0.2%
4	5	1.1%
5	1	0.2%
6	3	0.7%
7	1	0.2%
8	2	0.4%

Value	Count	Frequency (%)
0.75	2	0.4%
0.83	2	0.4%
1	3	0.7%
2	6	1.3%
3	1	0.2%
4	5	1.1%
5	1	0.2%
6	3	0.7%
7	1	0.2%
8	2	0.4%

Value	Count	Frequency (%)
0.42	1	0.2%
0.83	1	0.2%
0.92	1	0.2%
1	1	0.2%
2	4	0.9%
3	4	0.9%
4	5	1.1%
5	1	0.2%
6	1	0.2%
7	1	0.2%

SibSp
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	7
Distinct (%)	1.6%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.54035874	0.57847534

	Dataset A	Dataset B
Minimum	0	0
Maximum	8	8
Zeros	299	296
Zeros (%)	67.0%	66.4%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	1	1
95-th percentile	2	3
Maximum	8	8
Range	8	8
Interquartile range (IQR)	1	1

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	1.1205672	1.2111406
Coefficient of variation (CV)	2.0737468	2.0936771
Kurtosis	18.403109	16.227935
Mean	0.54035874	0.57847534
Median Absolute Deviation (MAD)	0	0
Skewness	3.7392648	3.6121318
Sum	241	258
Variance	1.2556709	1.4668615
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	299	67.0%
1	108	24.2%
2	17	3.8%
4	9	2.0%
3	7	1.6%
8	4	0.9%
5	2	0.4%

Value	Count	Frequency (%)
0	296	66.4%
1	111	24.9%
2	12	2.7%
4	11	2.5%
3	8	1.8%
8	5	1.1%
5	3	0.7%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	299	67.0%
1	108	24.2%
2	17	3.8%
3	7	1.6%
4	9	2.0%
5	2	0.4%
8	4	0.9%

Value	Count	Frequency (%)
0	296	66.4%
1	111	24.9%
2	12	2.7%
3	8	1.8%
4	11	2.5%
5	3	0.7%
8	5	1.1%

Value	Count	Frequency (%)
0	296	66.4%
1	111	24.9%
2	12	2.7%
3	8	1.8%
4	11	2.5%
5	3	0.7%
8	5	1.1%

Value	Count	Frequency (%)
0	299	67.0%
1	108	24.2%
2	17	3.8%
3	7	1.6%
4	9	2.0%
5	2	0.4%
8	4	0.9%

Parch
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	7
Distinct (%)	1.6%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.39237668	0.41479821

	Dataset A	Dataset B
Minimum	0	0
Maximum	6	6
Zeros	340	329
Zeros (%)	76.2%	73.8%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	0	1
95-th percentile	2	2
Maximum	6	6
Range	6	6
Interquartile range (IQR)	0	1

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	0.85896996	0.81868946
Coefficient of variation (CV)	2.1891463	1.9737054
Kurtosis	11.593563	9.3096702
Mean	0.39237668	0.41479821
Median Absolute Deviation (MAD)	0	0
Skewness	3.0187772	2.5834683
Sum	175	185
Variance	0.73782939	0.67025243
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	340	76.2%
1	59	13.2%
2	38	8.5%
5	4	0.9%
3	2	0.4%
4	2	0.4%
6	1	0.2%

Value	Count	Frequency (%)
0	329	73.8%
1	63	14.1%
2	48	10.8%
5	2	0.4%
3	2	0.4%
4	1	0.2%
6	1	0.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	340	76.2%
1	59	13.2%
2	38	8.5%
3	2	0.4%
4	2	0.4%
5	4	0.9%
6	1	0.2%

Value	Count	Frequency (%)
0	329	73.8%
1	63	14.1%
2	48	10.8%
3	2	0.4%
4	1	0.2%
5	2	0.4%
6	1	0.2%

Value	Count	Frequency (%)
0	329	73.8%
1	63	14.1%
2	48	10.8%
3	2	0.4%
4	1	0.2%
5	2	0.4%
6	1	0.2%

Value	Count	Frequency (%)
0	340	76.2%
1	59	13.2%
2	38	8.5%
3	2	0.4%
4	2	0.4%
5	4	0.9%
6	1	0.2%

Ticket
['Text', 'Text']

	Dataset A	Dataset B
Distinct	384	376
Distinct (%)	86.1%	84.3%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	18	18
Median length	17	17
Mean length	6.6726457	6.706278
Min length	3	3

Characters and Unicode

	Dataset A	Dataset B
Total characters	2976	2991
Distinct characters	35	34
Distinct categories	5	5 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	337	326 ?
Unique (%)	75.6%	73.1%

Sample

	Dataset A	Dataset B
1st row	364511	STON/O 2. 3101269
2nd row	315151	W./C. 14258
3rd row	36865	17453
4th row	PC 17582	113055
5th row	330958	240929

Value	Count	Frequency (%)
pc	34	6.0%
c.a	14	2.5%
ca	7	1.2%
a/5	7	1.2%
w./c	6	1.1%
382652	5	0.9%
14879	5	0.9%
sc/paris	5	0.9%
s.o.c	5	0.9%
f.c.c	4	0.7%
Other values (399)	472	83.7%

Value	Count	Frequency (%)
pc	32	5.7%
ca	9	1.6%
2	8	1.4%
ston/o	8	1.4%
c.a	8	1.4%
a/5	7	1.2%
sc/paris	6	1.1%
w./c	5	0.9%
a/4	5	0.9%
3101295	5	0.9%
Other values (398)	472	83.5%

Most occurring characters

Value	Count	Frequency (%)
3	375	12.6%
1	330	11.1%
2	297	10.0%
7	244	8.2%
4	235	7.9%
6	209	7.0%
0	209	7.0%
5	196	6.6%
9	169	5.7%
8	144	4.8%
Other values (25)	568	19.1%

Value	Count	Frequency (%)
3	361	12.1%
1	349	11.7%
2	296	9.9%
4	254	8.5%
7	237	7.9%
6	216	7.2%
0	204	6.8%
5	184	6.2%
9	166	5.5%
8	143	4.8%
Other values (24)	581	19.4%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2408	80.9%
Uppercase Letter	293	9.8%
Other Punctuation	144	4.8%
Space Separator	118	4.0%
Lowercase Letter	13	0.4%

Value	Count	Frequency (%)
Decimal Number	2410	80.6%
Uppercase Letter	310	10.4%
Other Punctuation	135	4.5%
Space Separator	119	4.0%
Lowercase Letter	17	0.6%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
3	375	15.6%
1	330	13.7%
2	297	12.3%
7	244	10.1%
4	235	9.8%
6	209	8.7%
0	209	8.7%
5	196	8.1%
9	169	7.0%
8	144	6.0%

Value	Count	Frequency (%)
3	361	15.0%
1	349	14.5%
2	296	12.3%
4	254	10.5%
7	237	9.8%
6	216	9.0%
0	204	8.5%
5	184	7.6%
9	166	6.9%
8	143	5.9%

Space Separator

Value	Count	Frequency (%)
	118	100.0%

Value	Count	Frequency (%)
	119	100.0%

Other Punctuation

Value	Count	Frequency (%)
.	103	71.5%
/	41	28.5%

Value	Count	Frequency (%)
.	86	63.7%
/	49	36.3%

Uppercase Letter

Value	Count	Frequency (%)
C	86	29.4%
P	44	15.0%
A	43	14.7%
O	32	10.9%
S	31	10.6%
N	13	4.4%
T	11	3.8%
W	8	2.7%
F	5	1.7%
I	5	1.7%
Other values (6)	15	5.1%

Value	Count	Frequency (%)
C	72	23.2%
P	55	17.7%
O	44	14.2%
A	39	12.6%
S	38	12.3%
T	16	5.2%
N	16	5.2%
W	10	3.2%
Q	5	1.6%
R	4	1.3%
Other values (5)	11	3.5%

Lowercase Letter

Value	Count	Frequency (%)
a	4	30.8%
s	3	23.1%
i	2	15.4%
r	2	15.4%
l	1	7.7%
e	1	7.7%

Value	Count	Frequency (%)
a	5	29.4%
s	4	23.5%
i	3	17.6%
r	3	17.6%
l	1	5.9%
e	1	5.9%

Most occurring scripts

Value	Count	Frequency (%)
Common	2670	89.7%
Latin	306	10.3%

Value	Count	Frequency (%)
Common	2664	89.1%
Latin	327	10.9%

Most frequent character per script

Common

Value	Count	Frequency (%)
3	375	14.0%
1	330	12.4%
2	297	11.1%
7	244	9.1%
4	235	8.8%
6	209	7.8%
0	209	7.8%
5	196	7.3%
9	169	6.3%
8	144	5.4%
Other values (3)	262	9.8%

Value	Count	Frequency (%)
3	361	13.6%
1	349	13.1%
2	296	11.1%
4	254	9.5%
7	237	8.9%
6	216	8.1%
0	204	7.7%
5	184	6.9%
9	166	6.2%
8	143	5.4%
Other values (3)	254	9.5%

Latin

Value	Count	Frequency (%)
C	86	28.1%
P	44	14.4%
A	43	14.1%
O	32	10.5%
S	31	10.1%
N	13	4.2%
T	11	3.6%
W	8	2.6%
F	5	1.6%
I	5	1.6%
Other values (12)	28	9.2%

Value	Count	Frequency (%)
C	72	22.0%
P	55	16.8%
O	44	13.5%
A	39	11.9%
S	38	11.6%
T	16	4.9%
N	16	4.9%
W	10	3.1%
a	5	1.5%
Q	5	1.5%
Other values (11)	27	8.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2976	100.0%

Value	Count	Frequency (%)
ASCII	2991	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	375	12.6%
1	330	11.1%
2	297	10.0%
7	244	8.2%
4	235	7.9%
6	209	7.0%
0	209	7.0%
5	196	6.6%
9	169	5.7%
8	144	4.8%
Other values (25)	568	19.1%

Value	Count	Frequency (%)
3	361	12.1%
1	349	11.7%
2	296	9.9%
4	254	8.5%
7	237	7.9%
6	216	7.2%
0	204	6.8%
5	184	6.2%
9	166	5.5%
8	143	4.8%
Other values (24)	581	19.4%

Fare
Real number (ℝ)

	Dataset A	Dataset B
Distinct	182	181
Distinct (%)	40.8%	40.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	30.867049	33.45979

	Dataset A	Dataset B
Minimum	0	0
Maximum	263	512.3292
Zeros	7	6
Zeros (%)	1.6%	1.3%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	7.2292	7.225
Q1	8.0344	8.05
median	15.5	15.3729
Q3	30.3927	34.03125
95-th percentile	112.18125	112.67708
Maximum	263	512.3292
Range	263	512.3292
Interquartile range (IQR)	22.3583	25.98125

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	41.324374	50.624524
Coefficient of variation (CV)	1.338786	1.5129959
Kurtosis	12.259916	37.88923
Mean	30.867049	33.45979
Median Absolute Deviation (MAD)	7.75	7.7979
Skewness	3.2356587	5.070449
Sum	13766.704	14923.066
Variance	1707.7039	2562.8424
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
8.05	22	4.9%
13	22	4.9%
7.8958	21	4.7%
26	19	4.3%
7.75	16	3.6%
10.5	11	2.5%
26.55	9	2.0%
7.925	8	1.8%
7.775	7	1.6%
0	7	1.6%
Other values (172)	304	68.2%

Value	Count	Frequency (%)
13	26	5.8%
8.05	23	5.2%
7.8958	16	3.6%
7.75	15	3.4%
26	15	3.4%
10.5	10	2.2%
26.55	9	2.0%
7.2292	7	1.6%
7.775	7	1.6%
7.225	7	1.6%
Other values (171)	311	69.7%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	7	1.6%
4.0125	1	0.2%
6.45	1	0.2%
6.75	1	0.2%
7.05	2	0.4%
7.0542	1	0.2%
7.125	1	0.2%
7.225	4	0.9%
7.2292	6	1.3%
7.25	6	1.3%

Value	Count	Frequency (%)
0	6	1.3%
4.0125	1	0.2%
6.2375	1	0.2%
6.4958	2	0.4%
6.75	2	0.4%
7.0458	1	0.2%
7.05	2	0.4%
7.125	3	0.7%
7.225	7	1.6%
7.2292	7	1.6%

Value	Count	Frequency (%)
0	6	1.3%
4.0125	1	0.2%
6.2375	1	0.2%
6.4958	2	0.4%
6.75	2	0.4%
7.0458	1	0.2%
7.05	2	0.4%
7.125	3	0.7%
7.225	7	1.6%
7.2292	7	1.6%

Value	Count	Frequency (%)
0	7	1.6%
4.0125	1	0.2%
6.45	1	0.2%
6.75	1	0.2%
7.05	2	0.4%
7.0542	1	0.2%
7.125	1	0.2%
7.225	4	0.9%
7.2292	6	1.3%
7.25	6	1.3%

Cabin
['Text', 'Text']

	Dataset A	Dataset B
Distinct	84	92
Distinct (%)	90.3%	88.5%
Missing	353	342
Missing (%)	79.1%	76.7%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	15	11
Median length	3	3
Mean length	3.5591398	3.4326923
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	331	357
Distinct characters	19	18
Distinct categories	3	3 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	75	82 ?
Unique (%)	80.6%	78.8%

Sample

	Dataset A	Dataset B
1st row	C91	C92
2nd row	C7	E17
3rd row	C99	B79
4th row	E40	E12
5th row	F2	B50

Value	Count	Frequency (%)
d	2	1.9%
b63	2	1.9%
c93	2	1.9%
d36	2	1.9%
b96	2	1.9%
c52	2	1.9%
b66	2	1.9%
b98	2	1.9%
b59	2	1.9%
b57	2	1.9%
Other values (84)	87	81.3%

Value	Count	Frequency (%)
d	3	2.6%
g6	3	2.6%
f	3	2.6%
g73	2	1.7%
c126	2	1.7%
b98	2	1.7%
b96	2	1.7%
d33	2	1.7%
b49	2	1.7%
b77	2	1.7%
Other values (92)	94	80.3%

Most occurring characters

Value	Count	Frequency (%)
C	33	10.0%
3	32	9.7%
2	30	9.1%
B	29	8.8%
6	24	7.3%
1	22	6.6%
5	21	6.3%
8	20	6.0%
9	18	5.4%
D	16	4.8%
Other values (9)	86	26.0%

Value	Count	Frequency (%)
1	40	11.2%
C	34	9.5%
2	31	8.7%
B	29	8.1%
3	27	7.6%
6	22	6.2%
D	21	5.9%
8	19	5.3%
0	19	5.3%
5	19	5.3%
Other values (8)	96	26.9%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	210	63.4%
Uppercase Letter	107	32.3%
Space Separator	14	4.2%

Value	Count	Frequency (%)
Decimal Number	227	63.6%
Uppercase Letter	117	32.8%
Space Separator	13	3.6%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
C	33	30.8%
B	29	27.1%
D	16	15.0%
E	13	12.1%
A	10	9.3%
F	4	3.7%
G	1	0.9%
T	1	0.9%

Value	Count	Frequency (%)
C	34	29.1%
B	29	24.8%
D	21	17.9%
E	16	13.7%
F	6	5.1%
G	6	5.1%
A	5	4.3%

Decimal Number

Value	Count	Frequency (%)
3	32	15.2%
2	30	14.3%
6	24	11.4%
1	22	10.5%
5	21	10.0%
8	20	9.5%
9	18	8.6%
4	16	7.6%
0	14	6.7%
7	13	6.2%

Value	Count	Frequency (%)
1	40	17.6%
2	31	13.7%
3	27	11.9%
6	22	9.7%
8	19	8.4%
0	19	8.4%
5	19	8.4%
9	17	7.5%
7	17	7.5%
4	16	7.0%

Space Separator

Value	Count	Frequency (%)
	14	100.0%

Value	Count	Frequency (%)
	13	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	224	67.7%
Latin	107	32.3%

Value	Count	Frequency (%)
Common	240	67.2%
Latin	117	32.8%

Most frequent character per script

Latin

Value	Count	Frequency (%)
C	33	30.8%
B	29	27.1%
D	16	15.0%
E	13	12.1%
A	10	9.3%
F	4	3.7%
G	1	0.9%
T	1	0.9%

Value	Count	Frequency (%)
C	34	29.1%
B	29	24.8%
D	21	17.9%
E	16	13.7%
F	6	5.1%
G	6	5.1%
A	5	4.3%

Common

Value	Count	Frequency (%)
3	32	14.3%
2	30	13.4%
6	24	10.7%
1	22	9.8%
5	21	9.4%
8	20	8.9%
9	18	8.0%
4	16	7.1%
0	14	6.2%
	14	6.2%

Value	Count	Frequency (%)
1	40	16.7%
2	31	12.9%
3	27	11.2%
6	22	9.2%
8	19	7.9%
0	19	7.9%
5	19	7.9%
9	17	7.1%
7	17	7.1%
4	16	6.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	331	100.0%

Value	Count	Frequency (%)
ASCII	357	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
C	33	10.0%
3	32	9.7%
2	30	9.1%
B	29	8.8%
6	24	7.3%
1	22	6.6%
5	21	6.3%
8	20	6.0%
9	18	5.4%
D	16	4.8%
Other values (9)	86	26.0%

Value	Count	Frequency (%)
1	40	11.2%
C	34	9.5%
2	31	8.7%
B	29	8.1%
3	27	7.6%
6	22	6.2%
D	21	5.9%
8	19	5.3%
0	19	5.3%
5	19	5.3%
Other values (8)	96	26.9%

Embarked
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	1	1
Missing (%)	0.2%	0.2%
Memory size	7.0 KiB	7.0 KiB

S	322
C	78
Q	45

S	325
C	90
Q	30

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	445	445
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	S	S
2nd row	S	S
3rd row	Q	C
4th row	S	S
5th row	Q	S

Common Values

Value	Count	Frequency (%)
S	322	72.2%
C	78	17.5%
Q	45	10.1%
(Missing)	1	0.2%

Value	Count	Frequency (%)
S	325	72.9%
C	90	20.2%
Q	30	6.7%
(Missing)	1	0.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
s	322	72.4%
c	78	17.5%
q	45	10.1%

Value	Count	Frequency (%)
s	325	73.0%
c	90	20.2%
q	30	6.7%

Most occurring characters

Value	Count	Frequency (%)
S	322	72.4%
C	78	17.5%
Q	45	10.1%

Value	Count	Frequency (%)
S	325	73.0%
C	90	20.2%
Q	30	6.7%

Most occurring categories

Value	Count	Frequency (%)
Uppercase Letter	445	100.0%

Value	Count	Frequency (%)
Uppercase Letter	445	100.0%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
S	322	72.4%
C	78	17.5%
Q	45	10.1%

Value	Count	Frequency (%)
S	325	73.0%
C	90	20.2%
Q	30	6.7%

Most occurring scripts

Value	Count	Frequency (%)
Latin	445	100.0%

Value	Count	Frequency (%)
Latin	445	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
S	322	72.4%
C	78	17.5%
Q	45	10.1%

Value	Count	Frequency (%)
S	325	73.0%
C	90	20.2%
Q	30	6.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	445	100.0%

Value	Count	Frequency (%)
ASCII	445	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
S	322	72.4%
C	78	17.5%
Q	45	10.1%

Value	Count	Frequency (%)
S	325	73.0%
C	90	20.2%
Q	30	6.7%

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset A

A simple visualization of nullity by column.

Dataset B

A simple visualization of nullity by column.

Dataset A

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset B

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows
Last rows

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
603	604	0	3	Torber, Mr. Ernst William	male	44.0	0	0	364511	8.0500	NaN	S
69	70	0	3	Kink, Mr. Vincenz	male	26.0	2	0	315151	8.6625	NaN	S
778	779	0	3	Kilgannon, Mr. Thomas J	male	NaN	0	0	36865	7.7375	NaN	Q
332	333	0	1	Graham, Mr. George Edward	male	38.0	0	1	PC 17582	153.4625	C91	S
44	45	1	3	Devaney, Miss. Margaret Delia	female	19.0	0	0	330958	7.8792	NaN	Q
153	154	0	3	van Billiard, Mr. Austin Blyler	male	40.5	0	2	A/5. 851	14.5000	NaN	S
561	562	0	3	Sivic, Mr. Husein	male	40.0	0	0	349251	7.8958	NaN	S
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
832	833	0	3	Saad, Mr. Amin	male	NaN	0	0	2671	7.2292	NaN	C
318	319	1	1	Wick, Miss. Mary Natalie	female	31.0	0	2	36928	164.8667	C7	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
414	415	1	3	Sundman, Mr. Johan Julian	male	44.00	0	0	STON/O 2. 3101269	7.9250	NaN	S
526	527	1	2	Ridsdale, Miss. Lucy	female	50.00	0	0	W./C. 14258	10.5000	NaN	S
453	454	1	1	Goldenberg, Mr. Samuel L	male	49.00	1	0	17453	89.1042	C92	C
857	858	1	1	Daly, Mr. Peter Denis	male	51.00	0	0	113055	26.5500	E17	S
399	400	1	2	Trout, Mrs. William H (Jessie L)	female	28.00	0	0	240929	12.6500	NaN	S
320	321	0	3	Dennis, Mr. Samuel	male	22.00	0	0	A/5 21172	7.2500	NaN	S
504	505	1	1	Maioni, Miss. Roberta	female	16.00	0	0	110152	86.5000	B79	S
831	832	1	2	Richards, Master. George Sibley	male	0.83	1	1	29106	18.7500	NaN	S
111	112	0	3	Zabour, Miss. Hileni	female	14.50	1	0	2665	14.4542	NaN	C
353	354	0	3	Arnold-Franchi, Mr. Josef	male	25.00	1	0	349237	17.8000	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
74	75	1	3	Bing, Mr. Lee	male	32.0	0	0	1601	56.4958	NaN	S
594	595	0	2	Chapman, Mr. John Henry	male	37.0	1	0	SC/AH 29037	26.0000	NaN	S
374	375	0	3	Palsson, Miss. Stina Viola	female	3.0	3	1	349909	21.0750	NaN	S
147	148	0	3	Ford, Miss. Robina Maggie "Ruby"	female	9.0	2	2	W./C. 6608	34.3750	NaN	S
722	723	0	2	Gillespie, Mr. William Henry	male	34.0	0	0	12233	13.0000	NaN	S
676	677	0	3	Sawyer, Mr. Frederick Charles	male	24.5	0	0	342826	8.0500	NaN	S
407	408	1	2	Richards, Master. William Rowe	male	3.0	1	1	29106	18.7500	NaN	S
152	153	0	3	Meo, Mr. Alfonzo	male	55.5	0	0	A.5. 11206	8.0500	NaN	S
690	691	1	1	Dick, Mr. Albert Adrian	male	31.0	1	0	17474	57.0000	B20	S
488	489	0	3	Somerton, Mr. Francis William	male	30.0	0	0	A.5. 18509	8.0500	NaN	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
439	440	0	2	Kvillner, Mr. Johan Henrik Johannesson	male	31.0	0	0	C.A. 18723	10.5000	NaN	S
695	696	0	2	Chapman, Mr. Charles Henry	male	52.0	0	0	248731	13.5000	NaN	S
372	373	0	3	Beavan, Mr. William Thomas	male	19.0	0	0	323951	8.0500	NaN	S
858	859	1	3	Baclini, Mrs. Solomon (Latifa Qurban)	female	24.0	0	3	2666	19.2583	NaN	C
290	291	1	1	Barber, Miss. Ellen "Nellie"	female	26.0	0	0	19877	78.8500	NaN	S
334	335	1	1	Frauenthal, Mrs. Henry William (Clara Heinsheimer)	female	NaN	1	0	PC 17611	133.6500	NaN	S
141	142	1	3	Nysten, Miss. Anna Sofia	female	22.0	0	0	347081	7.7500	NaN	S
269	270	1	1	Bissette, Miss. Amelia	female	35.0	0	0	PC 17760	135.6333	C99	S
355	356	0	3	Vanden Steen, Mr. Leo Peter	male	28.0	0	0	345783	9.5000	NaN	S
288	289	1	2	Hosono, Mr. Masabumi	male	42.0	0	0	237798	13.0000	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Overview

Variables

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters

Most occurring categories

Most frequent character per category

Space Separator

Lowercase Letter

Uppercase Letter

Other Punctuation

Open Punctuation

Close Punctuation

Dash Punctuation

Most occurring scripts

Most frequent character per script

Common

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Space Separator

Other Punctuation

Uppercase Letter

Lowercase Letter

Most occurring scripts

Most frequent character per script

Common

Latin

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters